RFM Analysis Project 1

import pandas as pd
import plotly.express as px
import plotly.io as pio
import plotly.graph_objects as go
pio.templates.default = "plotly_white"

data = pd.read_csv("C:/Users/Kingsley Mills/Desktop/pretech/DS PROJECTS/RFM Analysis/rfm_data.csv")
data.head()

	CustomerID	PurchaseDate	TransactionAmount	ProductInformation	OrderID	Location
0	8814	2023-04-11	943.31	Product C	890075	Tokyo
1	2188	2023-04-11	463.70	Product A	176819	London
2	4608	2023-04-11	80.28	Product A	340062	New York
3	2559	2023-04-11	221.29	Product A	239145	London
4	9482	2023-04-11	739.56	Product A	194545	Paris

print(data.isnull().sum())

CustomerID            0
PurchaseDate          0
TransactionAmount     0
ProductInformation    0
OrderID               0
Location              0
dtype: int64

data.dropna(inplace=True)

data.shape

(1000, 6)

print(data.isnull().sum())

CustomerID            0
PurchaseDate          0
TransactionAmount     0
ProductInformation    0
OrderID               0
Location              0
dtype: int64

from datetime import datetime

# Convert 'PurchaseDate' to datetime
data['PurchaseDate'] = pd.to_datetime(data['PurchaseDate'])

# Calculate Recency
data['Recency'] = (datetime.now().date() - data['PurchaseDate'].dt.date).dt.days

# Calculate Frequency
frequency_data = data.groupby('CustomerID')['OrderID'].count().reset_index()
frequency_data.rename(columns={'OrderID': 'Frequency'}, inplace=True)
data = data.merge(frequency_data, on='CustomerID', how='left')

# Calculate Monetary Value
monetary_data = data.groupby('CustomerID')['TransactionAmount'].sum().reset_index()
monetary_data.rename(columns={'TransactionAmount': 'MonetaryValue'}, inplace=True)
data = data.merge(monetary_data, on='CustomerID', how='left')

print(data.head())

   CustomerID PurchaseDate  TransactionAmount ProductInformation  OrderID  \
0        8814   2023-04-11             943.31          Product C   890075   
1        2188   2023-04-11             463.70          Product A   176819   
2        4608   2023-04-11              80.28          Product A   340062   
3        2559   2023-04-11             221.29          Product A   239145   
4        9482   2023-04-11             739.56          Product A   194545   

   Location  Recency  Frequency  MonetaryValue  
0     Tokyo      275          1         943.31  
1    London      275          1         463.70  
2  New York      275          1          80.28  
3    London      275          1         221.29  
4     Paris      275          1         739.56  

# Define scoring criteria for each RFM value
recency_scores = [5, 4, 3, 2, 1]  # Higher score for lower recency (more recent)
frequency_scores = [1, 2, 3, 4, 5]  # Higher score for higher frequency
monetary_scores = [1, 2, 3, 4, 5]  # Higher score for higher monetary value

# Calculate RFM scores
data['RecencyScore'] = pd.cut(data['Recency'], bins=5, labels=recency_scores)
data['FrequencyScore'] = pd.cut(data['Frequency'], bins=5, labels=frequency_scores)
data['MonetaryScore'] = pd.cut(data['MonetaryValue'], bins=5, labels=monetary_scores)

# Convert RFM scores to numeric type
data['RecencyScore'] = data['RecencyScore'].astype(int)
data['FrequencyScore'] = data['FrequencyScore'].astype(int)
data['MonetaryScore'] = data['MonetaryScore'].astype(int)

print(data.head())

   CustomerID PurchaseDate  TransactionAmount ProductInformation  OrderID  \
0        8814   2023-04-11             943.31          Product C   890075   
1        2188   2023-04-11             463.70          Product A   176819   
2        4608   2023-04-11              80.28          Product A   340062   
3        2559   2023-04-11             221.29          Product A   239145   
4        9482   2023-04-11             739.56          Product A   194545   

   Location  Recency  Frequency  MonetaryValue  RecencyScore  FrequencyScore  \
0     Tokyo      275          1         943.31             1               1   
1    London      275          1         463.70             1               1   
2  New York      275          1          80.28             1               1   
3    London      275          1         221.29             1               1   
4     Paris      275          1         739.56             1               1   

   MonetaryScore  
0              2  
1              1  
2              1  
3              1  
4              2  

# Calculate RFM score by combining the individual scores
data['RFM_Score'] = data['RecencyScore'] + data['FrequencyScore'] + data['MonetaryScore']

# Create RFM segments based on the RFM score
segment_labels = ['Low-Value', 'Mid-Value', 'High-Value']
data['Value Segment'] = pd.qcut(data['RFM_Score'], q=3, labels=segment_labels)

# RFM Segment Distribution
segment_counts = data['Value Segment'].value_counts().reset_index()
segment_counts.columns = ['Value Segment', 'Count']

pastel_colors = px.colors.qualitative.Pastel

# Create the bar chart
fig_segment_dist = px.bar(segment_counts, x='Value Segment', y='Count', 
                          color='Value Segment', color_discrete_sequence=pastel_colors,
                          title='RFM Value Segment Distribution')

# Update the layout
fig_segment_dist.update_layout(xaxis_title='RFM Value Segment',
                              yaxis_title='Count',
                              showlegend=False)

# Show the figure
fig_segment_dist.show()

# Create a new column for RFM Customer Segments
data['RFM Customer Segments'] = ''

# Assign RFM segments based on the RFM score
data.loc[data['RFM_Score'] >= 9, 'RFM Customer Segments'] = 'Champions'
data.loc[(data['RFM_Score'] >= 6) & (data['RFM_Score'] < 9), 'RFM Customer Segments'] = 'Potential Loyalists'
data.loc[(data['RFM_Score'] >= 5) & (data['RFM_Score'] < 6), 'RFM Customer Segments'] = 'At Risk Customers'
data.loc[(data['RFM_Score'] >= 4) & (data['RFM_Score'] < 5), 'RFM Customer Segments'] = "Can't Lose"
data.loc[(data['RFM_Score'] >= 3) & (data['RFM_Score'] < 4), 'RFM Customer Segments'] = "Lost"

# Print the updated data with RFM segments
print(data[['CustomerID', 'RFM Customer Segments']])

     CustomerID RFM Customer Segments
0          8814            Can't Lose
1          2188                  Lost
2          4608                  Lost
3          2559                  Lost
4          9482            Can't Lose
..          ...                   ...
995        2970   Potential Loyalists
996        6669   Potential Loyalists
997        8836   Potential Loyalists
998        1440   Potential Loyalists
999        4759   Potential Loyalists

[1000 rows x 2 columns]

segment_product_counts = data.groupby(['Value Segment', 'RFM Customer Segments']).size().reset_index(name='Count')

segment_product_counts = segment_product_counts.sort_values('Count', ascending=False)

fig_treemap_segment_product = px.treemap(segment_product_counts, 
                                         path=['Value Segment', 'RFM Customer Segments'], 
                                         values='Count',
                                         color='Value Segment', color_discrete_sequence=px.colors.qualitative.Pastel,
                                         title='RFM Customer Segments by Value')
fig_treemap_segment_product.show()

# Filter the data to include only the customers in the Champions segment
champions_segment = data[data['RFM Customer Segments'] == 'Champions']

fig = go.Figure()
fig.add_trace(go.Box(y=champions_segment['RecencyScore'], name='Recency'))
fig.add_trace(go.Box(y=champions_segment['FrequencyScore'], name='Frequency'))
fig.add_trace(go.Box(y=champions_segment['MonetaryScore'], name='Monetary'))

fig.update_layout(title='Distribution of RFM Values within Champions Segment',
                  yaxis_title='RFM Value',
                  showlegend=True)

fig.show()

correlation_matrix = champions_segment[['RecencyScore', 'FrequencyScore', 'MonetaryScore']].corr()

# Visualize the correlation matrix using a heatmap
fig_heatmap = go.Figure(data=go.Heatmap(
                   z=correlation_matrix.values,
                   x=correlation_matrix.columns,
                   y=correlation_matrix.columns,
                   colorscale='RdBu',
                   colorbar=dict(title='Correlation')))

fig_heatmap.update_layout(title='Correlation Matrix of RFM Values within Champions Segment')

fig_heatmap.show()

import plotly.colors

pastel_colors = plotly.colors.qualitative.Pastel

segment_counts = data['RFM Customer Segments'].value_counts()

# Create a bar chart to compare segment counts
fig = go.Figure(data=[go.Bar(x=segment_counts.index, y=segment_counts.values,
                            marker=dict(color=pastel_colors))])

# Set the color of the Champions segment as a different color
champions_color = 'rgb(158, 202, 225)'
fig.update_traces(marker_color=[champions_color if segment == 'Champions' else pastel_colors[i]
                                for i, segment in enumerate(segment_counts.index)],
                  marker_line_color='rgb(8, 48, 107)',
                  marker_line_width=1.5, opacity=0.6)

# Update the layout
fig.update_layout(title='Comparison of RFM Segments',
                  xaxis_title='RFM Segments',
                  yaxis_title='Number of Customers',
                  showlegend=False)

fig.show()

# Calculate the average Recency, Frequency, and Monetary scores for each segment
segment_scores = data.groupby('RFM Customer Segments')['RecencyScore', 'FrequencyScore', 'MonetaryScore'].mean().reset_index()

# Create a grouped bar chart to compare segment scores
fig = go.Figure()

# Add bars for Recency score
fig.add_trace(go.Bar(
    x=segment_scores['RFM Customer Segments'],
    y=segment_scores['RecencyScore'],
    name='Recency Score',
    marker_color='rgb(158,202,225)'
))

# Add bars for Frequency score
fig.add_trace(go.Bar(
    x=segment_scores['RFM Customer Segments'],
    y=segment_scores['FrequencyScore'],
    name='Frequency Score',
    marker_color='rgb(94,158,217)'
))

# Add bars for Monetary score
fig.add_trace(go.Bar(
    x=segment_scores['RFM Customer Segments'],
    y=segment_scores['MonetaryScore'],
    name='Monetary Score',
    marker_color='rgb(32,102,148)'
))

# Update the layout
fig.update_layout(
    title='Comparison of RFM Segments based on Recency, Frequency, and Monetary Scores',
    xaxis_title='RFM Segments',
    yaxis_title='Score',
    barmode='group',
    showlegend=True
)

fig.show()

C:\Users\KINGSL~1\AppData\Local\Temp/ipykernel_16248/1663265694.py:2: FutureWarning:

Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.